-No Collision/poor collision on NERFs and GS: to have a proper interactive world, you usually need accurate character collision so that your character or vehicle can move along the floor/ground (as opposed to falling thru it) run into walls, go through door frames, etc. NERFs suffer from the same issues as photogrammetry in that they need “structure from motion” (COLMAP or similar) to give them a mesh or 3-D output that can be meshed for collision to register off of. The mesh from reality capture is noisy, and is not simple geometry. Think millions of triangles from a laser scanner or camera for “flat” ground that a video game would use 100 triangles for.
-Scanning: there’s no scanner available that provides both good 3-D information and good photo realistic textures at a price people will want to pay. Scanning every square inch of playable space in even a modest sized house is a pain, and people will look behind the television, underneath the furniture and everywhere else that most of these scanning videos and demos never go. There are a lot of ugly angles that these videos omit where a player would go.
-Post Processing: of you scan your house or any other real space, you will have poor lighting unless you took the time to do your own custom lighting and color setup. That will all need to be corrected in post process so that you can dynamically light your environment. Lighting is one of the most next generation things that people associate with games and you will be fighting prebaked shadows throughout the entire house or area that you have scanned. You don’t get away from this with NERFs or gaussian splats, because those scenes also have prebaked lighting in them that is static.
Object Destruction and Physics: I Love the game teardown, and if you want to see what it’s like to actually bust up and destroy structures that have been physically scanned, there is a plug-in to import reality capture models directly into the game with a little bit of modding. That said, teardown is voxel based, and is one of the most advanced engines that has been built to do such a thing. I have seen nothing else capable of doing cool looking destruction of any object, scanned or 3D modeled, without a large studio effort and a ton of optimization.
Lighting is the big issue, IMO. As soon as you want any kind of interactivity besides moving the camera you need dynamic lighting. The problem is you're going to have to mix the captured absolutely perfect real-world lighting with extremely approximate real-time computed lighting (which will be much worse than offline-rendered path tracing, which still wouldn't match real-world quality). It's going to look awful. At least, until someone figures out a revolutionary neural relighting system. We are pretty far from that today.
Scale is another issue. Two issues, really, rendering and storage. There's already a lot of research into scaling up rendering to large and detailed scenes, but I wouldn't say it's solved yet. And once you have rendering, storage will be the next issue. These scans will be massive and we'll need some very effective compression to be able to distribute large scenes to users.
The collision detection problem is related heavily to having clean 3D as mentioned above. My company is doing development on computing collision on reality capture right now in a clean way and I would be interested in any thoughts you have. We are chunking collision on the dataset at a fixed distance from the player character (can’t go too fast in a vehicle or it will outpace the collision and fall thru the floor) and have a tunable LOD that influences collision resolution.
Frankly I’m surprised that I can’t easily make crude 3D models of spaces with a simple app presently. It seems well within the capabilities of the hardware and software.
In other words, if you've got a consistent 3D geometric map of the house with textures, then you can do a pass in the morning with only daylight, midday only daylight, late afternoon only daylight, and then one at night with artificial light.
If you're dealing with textures that map onto identical geometries (and assume no objects move during the day), it seems like it ought to be relatively straightforward to train AI's to produce a flat unlit texture version, especially since you can train them on easily generated raytraced renderings. There might even be straight-up statistical methods to do it.
So I think it not the lighting itself that is the biggest problem -- it's having the clean consistent geometries in the first place.
It wasn't very well done but I figured out how to make the basic walls and building, add stairs, add some windows, grab some pre existing props like simple couches beds and a TV, and it was pretty recognizable. After adding a couple ladders to the outside so you could climb in the windows or on the roof the map was super fun just as a map, and doubly so since I could do things like hide in my own bedroom closet and recognize the rooms.
Took some work since I didn't know how to do anything but totally worth it. I feel like there has to be a much more accessible level editor in some game out there today, not sure what it would be though.
I thought my school had great architecture for another map but someone rightfully convinced me that would be a very bad idea to add to a shooting game. So I never made any others besides the house.
NeRF is not that, it's just a way to represent and render volumetric objects. It's like 10% of what makes a game. Eventually, in theory, it might be possible to make NeRFs or another similar representation animated, interactive, or even entirely drivable by an end-to-end model. But the current state is so far from it that it isn't worth speculating about.
What you want is doable with classic tools already.
The main exception I can think of is in racing simulators, it's already common for the developers of those to drive LiDAR cars around real-world tracks and use that data to build a 1:1 replica for their game. NeRF might be a natural extension of that if they can figure out a way to combine it with dynamic lighting and weather conditions.
Recognising objects for what they are has only recently become somewhat possible. Separating them in a 3D scan is still pretty much impossible.